Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations4943
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory965.6 KiB
Average record size in memory200.0 B

Variable types

Numeric17
Categorical7
Text1

Alerts

annualHomeownersInsurance is highly overall correlated with bathrooms and 7 other fieldsHigh correlation
bathrooms is highly overall correlated with annualHomeownersInsurance and 5 other fieldsHigh correlation
bedrooms is highly overall correlated with annualHomeownersInsurance and 5 other fieldsHigh correlation
countyFIPS is highly overall correlated with stateHigh correlation
homeType_CONDO is highly overall correlated with annualHomeownersInsurance and 4 other fieldsHigh correlation
homeType_LOT is highly overall correlated with zestimateHighPercent and 1 other fieldsHigh correlation
homeType_SINGLE_FAMILY is highly overall correlated with annualHomeownersInsurance and 2 other fieldsHigh correlation
latitude is highly overall correlated with stateHigh correlation
livingArea is highly overall correlated with annualHomeownersInsurance and 5 other fieldsHigh correlation
longitude is highly overall correlated with stateHigh correlation
price is highly overall correlated with annualHomeownersInsurance and 7 other fieldsHigh correlation
propertyTaxRate is highly overall correlated with stateHigh correlation
rentZestimate is highly overall correlated with annualHomeownersInsurance and 6 other fieldsHigh correlation
state is highly overall correlated with countyFIPS and 4 other fieldsHigh correlation
zestimate is highly overall correlated with annualHomeownersInsurance and 6 other fieldsHigh correlation
zestimateHighPercent is highly overall correlated with homeType_LOT and 1 other fieldsHigh correlation
zestimateLowPercent is highly overall correlated with homeType_LOT and 1 other fieldsHigh correlation
zipcode is highly overall correlated with stateHigh correlation
state is highly imbalanced (96.5%)Imbalance
homeType_APARTMENT is highly imbalanced (92.7%)Imbalance
homeType_LOT is highly imbalanced (98.5%)Imbalance
homeType_MANUFACTURED is highly imbalanced (97.7%)Imbalance
homeType_MULTI_FAMILY is highly imbalanced (58.7%)Imbalance
monthlyHoaFee is highly skewed (γ1 = 66.59234387)Skewed
monthlyHoaFee has 3954 (80.0%) zerosZeros

Reproduction

Analysis started2024-10-14 17:53:36.830986
Analysis finished2024-10-14 17:54:51.423087
Duration1 minute and 14.59 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct3695
Distinct (%)74.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-148.97898
Minimum-150.01093
Maximum-70.4831
Zeros0
Zeros (%)0.0%
Negative4943
Negative (%)100.0%
Memory size38.7 KiB
2024-10-14T20:54:51.653283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-150.01093
5-th percentile-149.9591
Q1-149.92893
median-149.87282
Q3-149.81335
95-th percentile-149.72786
Maximum-70.4831
Range79.52783
Interquartile range (IQR)0.115585

Descriptive statistics

Standard deviation7.6134825
Coefficient of variation (CV)-0.051104409
Kurtosis75.159795
Mean-148.97898
Median Absolute Deviation (MAD)0.05776
Skewness8.693605
Sum-736403.08
Variance57.965116
MonotonicityNot monotonic
2024-10-14T20:54:51.974579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-149.73212 13
 
0.3%
-149.94147 13
 
0.3%
-149.89989 12
 
0.2%
-149.92009 12
 
0.2%
-149.72975 11
 
0.2%
-149.82962 10
 
0.2%
-149.88034 8
 
0.2%
-149.89291 8
 
0.2%
-149.89194 7
 
0.1%
-149.94337 7
 
0.1%
Other values (3685) 4842
98.0%
ValueCountFrequency (%)
-150.01093 1
< 0.1%
-150.0097 1
< 0.1%
-150.00902 1
< 0.1%
-150.00879 1
< 0.1%
-150.00493 1
< 0.1%
-150.0028 1
< 0.1%
-150.00247 1
< 0.1%
-150.00201 1
< 0.1%
-150.00186 1
< 0.1%
-150.00182 1
< 0.1%
ValueCountFrequency (%)
-70.4831 1
< 0.1%
-70.483406 2
< 0.1%
-71.42977 1
< 0.1%
-72.25583 1
< 0.1%
-73.088585 1
< 0.1%
-73.171455 1
< 0.1%
-73.702896 1
< 0.1%
-73.819725 1
< 0.1%
-73.860085 1
< 0.1%
-73.893456 1
< 0.1%

countyFIPS
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2315.6472
Minimum1117
Maximum55079
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-10-14T20:54:52.200847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1117
5-th percentile2020
Q12020
median2020
Q32020
95-th percentile2020
Maximum55079
Range53962
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3049.2515
Coefficient of variation (CV)1.3168032
Kurtosis156.19322
Mean2315.6472
Median Absolute Deviation (MAD)0
Skewness12.03186
Sum11446244
Variance9297934.8
MonotonicityNot monotonic
2024-10-14T20:54:52.426446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
2020 4875
98.6%
6065 4
 
0.1%
12015 4
 
0.1%
34005 4
 
0.1%
55079 3
 
0.1%
10005 3
 
0.1%
26125 3
 
0.1%
19155 2
 
< 0.1%
9001 2
 
< 0.1%
37071 2
 
< 0.1%
Other values (34) 41
 
0.8%
ValueCountFrequency (%)
1117 2
 
< 0.1%
2020 4875
98.6%
6059 2
 
< 0.1%
6065 4
 
0.1%
6071 2
 
< 0.1%
8031 1
 
< 0.1%
9001 2
 
< 0.1%
9009 1
 
< 0.1%
10005 3
 
0.1%
12011 2
 
< 0.1%
ValueCountFrequency (%)
55079 3
0.1%
48491 1
 
< 0.1%
48245 1
 
< 0.1%
47093 1
 
< 0.1%
42095 1
 
< 0.1%
42077 1
 
< 0.1%
42049 1
 
< 0.1%
40037 1
 
< 0.1%
39155 1
 
< 0.1%
37071 2
< 0.1%

monthlyHoaFee
Real number (ℝ)

SKEWED  ZEROS 

Distinct213
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.672871
Minimum0
Maximum45929
Zeros3954
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-10-14T20:54:52.665149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile348
Maximum45929
Range45929
Interquartile range (IQR)0

Descriptive statistics

Standard deviation664.56376
Coefficient of variation (CV)11.522987
Kurtosis4595.8062
Mean57.672871
Median Absolute Deviation (MAD)0
Skewness66.592344
Sum285077
Variance441644.99
MonotonicityNot monotonic
2024-10-14T20:54:52.956354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3954
80.0%
18 29
 
0.6%
25 28
 
0.6%
90 24
 
0.5%
300 23
 
0.5%
400 21
 
0.4%
115 20
 
0.4%
265 19
 
0.4%
272 17
 
0.3%
338 16
 
0.3%
Other values (203) 792
 
16.0%
ValueCountFrequency (%)
0 3954
80.0%
4 13
 
0.3%
6 5
 
0.1%
8 5
 
0.1%
10 2
 
< 0.1%
11 6
 
0.1%
12 14
 
0.3%
13 12
 
0.2%
15 11
 
0.2%
16 5
 
0.1%
ValueCountFrequency (%)
45929 1
 
< 0.1%
1307 4
0.1%
1191 1
 
< 0.1%
1114 1
 
< 0.1%
873 2
 
< 0.1%
830 1
 
< 0.1%
752 4
0.1%
691 1
 
< 0.1%
681 6
0.1%
664 1
 
< 0.1%

annualHomeownersInsurance
Real number (ℝ)

HIGH CORRELATION 

Distinct2074
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1681.3073
Minimum5
Maximum11550
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-10-14T20:54:53.145147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile660.1
Q11245
median1630
Q31972
95-th percentile2894.4
Maximum11550
Range11545
Interquartile range (IQR)727

Descriptive statistics

Standard deviation740.1802
Coefficient of variation (CV)0.44024088
Kurtosis13.627176
Mean1681.3073
Median Absolute Deviation (MAD)362
Skewness2.1059821
Sum8310702
Variance547866.74
MonotonicityNot monotonic
2024-10-14T20:54:53.366261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1630 14
 
0.3%
1733 12
 
0.2%
1633 12
 
0.2%
1743 11
 
0.2%
1691 11
 
0.2%
1341 11
 
0.2%
1677 10
 
0.2%
1696 10
 
0.2%
1762 9
 
0.2%
1310 9
 
0.2%
Other values (2064) 4834
97.8%
ValueCountFrequency (%)
5 1
< 0.1%
252 1
< 0.1%
335 1
< 0.1%
366 1
< 0.1%
378 2
< 0.1%
382 1
< 0.1%
384 1
< 0.1%
388 1
< 0.1%
394 1
< 0.1%
397 1
< 0.1%
ValueCountFrequency (%)
11550 1
< 0.1%
8646 1
< 0.1%
8004 1
< 0.1%
7710 1
< 0.1%
6688 1
< 0.1%
6539 1
< 0.1%
6274 1
< 0.1%
6273 1
< 0.1%
6143 1
< 0.1%
6071 1
< 0.1%

state
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct27
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size38.7 KiB
AK
4875 
FL
 
8
TX
 
7
CA
 
6
NY
 
5
Other values (22)
 
42

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters9886
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.2%

Sample

1st rowAK
2nd rowAK
3rd rowAK
4th rowAK
5th rowAK

Common Values

ValueCountFrequency (%)
AK 4875
98.6%
FL 8
 
0.2%
TX 7
 
0.1%
CA 6
 
0.1%
NY 5
 
0.1%
MI 4
 
0.1%
NJ 3
 
0.1%
MA 3
 
0.1%
NC 3
 
0.1%
CT 3
 
0.1%
Other values (17) 26
 
0.5%

Length

2024-10-14T20:54:53.563895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ak 4875
98.6%
fl 8
 
0.2%
tx 7
 
0.1%
ca 6
 
0.1%
ny 5
 
0.1%
mi 4
 
0.1%
nj 3
 
0.1%
ma 3
 
0.1%
nc 3
 
0.1%
ct 3
 
0.1%
Other values (17) 26
 
0.5%

Most occurring characters

ValueCountFrequency (%)
A 4891
49.5%
K 4878
49.3%
C 16
 
0.2%
N 13
 
0.1%
T 12
 
0.1%
L 11
 
0.1%
M 11
 
0.1%
I 9
 
0.1%
F 8
 
0.1%
X 7
 
0.1%
Other values (11) 30
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9886
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 4891
49.5%
K 4878
49.3%
C 16
 
0.2%
N 13
 
0.1%
T 12
 
0.1%
L 11
 
0.1%
M 11
 
0.1%
I 9
 
0.1%
F 8
 
0.1%
X 7
 
0.1%
Other values (11) 30
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9886
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 4891
49.5%
K 4878
49.3%
C 16
 
0.2%
N 13
 
0.1%
T 12
 
0.1%
L 11
 
0.1%
M 11
 
0.1%
I 9
 
0.1%
F 8
 
0.1%
X 7
 
0.1%
Other values (11) 30
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9886
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 4891
49.5%
K 4878
49.3%
C 16
 
0.2%
N 13
 
0.1%
T 12
 
0.1%
L 11
 
0.1%
M 11
 
0.1%
I 9
 
0.1%
F 8
 
0.1%
X 7
 
0.1%
Other values (11) 30
 
0.3%

yearBuilt
Real number (ℝ)

Distinct91
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1976.1088
Minimum1880
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-10-14T20:54:53.768063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1880
5-th percentile1952
Q11969
median1977
Q31983
95-th percentile1998
Maximum2022
Range142
Interquartile range (IQR)14

Descriptive statistics

Standard deviation13.165797
Coefficient of variation (CV)0.0066624858
Kurtosis1.6923806
Mean1976.1088
Median Absolute Deviation (MAD)6
Skewness-0.19036234
Sum9767906
Variance173.33821
MonotonicityNot monotonic
2024-10-14T20:54:54.073155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1983 473
 
9.6%
1982 340
 
6.9%
1978 236
 
4.8%
1975 208
 
4.2%
1984 197
 
4.0%
1974 190
 
3.8%
1977 182
 
3.7%
1972 181
 
3.7%
1981 167
 
3.4%
1976 162
 
3.3%
Other values (81) 2607
52.7%
ValueCountFrequency (%)
1880 1
 
< 0.1%
1900 2
 
< 0.1%
1911 1
 
< 0.1%
1915 1
 
< 0.1%
1920 1
 
< 0.1%
1930 1
 
< 0.1%
1935 1
 
< 0.1%
1938 2
 
< 0.1%
1939 4
0.1%
1940 5
0.1%
ValueCountFrequency (%)
2022 1
 
< 0.1%
2021 2
 
< 0.1%
2020 15
0.3%
2019 8
0.2%
2018 6
 
0.1%
2017 2
 
< 0.1%
2016 2
 
< 0.1%
2015 3
 
0.1%
2013 2
 
< 0.1%
2012 2
 
< 0.1%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct4014
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.825243
Minimum26.004696
Maximum61.231228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-10-14T20:54:54.330226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum26.004696
5-th percentile61.121301
Q161.146248
median61.172043
Q361.19737
95-th percentile61.219246
Maximum61.231228
Range35.226532
Interquartile range (IQR)0.051122

Descriptive statistics

Standard deviation2.9797727
Coefficient of variation (CV)0.04898908
Kurtosis79.662601
Mean60.825243
Median Absolute Deviation (MAD)0.025612
Skewness-8.8518289
Sum300659.18
Variance8.8790454
MonotonicityNot monotonic
2024-10-14T20:54:54.584529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.18444 13
 
0.3%
61.180492 12
 
0.2%
61.21068 12
 
0.2%
61.222507 11
 
0.2%
61.1795 10
 
0.2%
61.20454 10
 
0.2%
61.202625 10
 
0.2%
61.182777 9
 
0.2%
61.20415 9
 
0.2%
61.204155 7
 
0.1%
Other values (4004) 4840
97.9%
ValueCountFrequency (%)
26.004696 1
< 0.1%
26.10778 1
< 0.1%
26.15767 1
< 0.1%
26.527695 1
< 0.1%
26.899212 1
< 0.1%
27.343218 1
< 0.1%
27.510103 1
< 0.1%
28.249554 1
< 0.1%
28.95348 1
< 0.1%
29.595736 1
< 0.1%
ValueCountFrequency (%)
61.231228 1
< 0.1%
61.2311 1
< 0.1%
61.231094 1
< 0.1%
61.23106 1
< 0.1%
61.23081 1
< 0.1%
61.2308 1
< 0.1%
61.23069 1
< 0.1%
61.23066 1
< 0.1%
61.23053 1
< 0.1%
61.2305 1
< 0.1%

rentZestimate
Real number (ℝ)

HIGH CORRELATION 

Distinct2176
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2647.388
Minimum782
Maximum11544
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-10-14T20:54:54.808268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum782
5-th percentile1593.1
Q12176.5
median2607
Q33015
95-th percentile3931.8
Maximum11544
Range10762
Interquartile range (IQR)838.5

Descriptive statistics

Standard deviation733.2617
Coefficient of variation (CV)0.27697553
Kurtosis5.9702442
Mean2647.388
Median Absolute Deviation (MAD)419
Skewness1.1525559
Sum13086039
Variance537672.72
MonotonicityNot monotonic
2024-10-14T20:54:55.032440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2804 12
 
0.2%
1646 11
 
0.2%
1645 11
 
0.2%
2594 10
 
0.2%
1744 10
 
0.2%
1601 10
 
0.2%
2701 9
 
0.2%
1664 9
 
0.2%
1565 9
 
0.2%
1824 9
 
0.2%
Other values (2166) 4843
98.0%
ValueCountFrequency (%)
782 1
< 0.1%
846 1
< 0.1%
903 1
< 0.1%
1046 1
< 0.1%
1083 1
< 0.1%
1089 1
< 0.1%
1110 1
< 0.1%
1125 1
< 0.1%
1174 1
< 0.1%
1175 1
< 0.1%
ValueCountFrequency (%)
11544 1
< 0.1%
7545 1
< 0.1%
6515 1
< 0.1%
6253 1
< 0.1%
6072 1
< 0.1%
6023 1
< 0.1%
5999 1
< 0.1%
5785 1
< 0.1%
5783 1
< 0.1%
5734 1
< 0.1%

city
Text

Distinct64
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size38.7 KiB
2024-10-14T20:54:55.378318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length17
Median length9
Mean length9.0040461
Min length4

Characters and Unicode

Total characters44507
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)1.2%

Sample

1st rowAnchorage
2nd rowAnchorage
3rd rowAnchorage
4th rowAnchorage
5th rowAnchorage
ValueCountFrequency (%)
anchorage 4875
98.2%
new 4
 
0.1%
seabury 3
 
0.1%
highland 2
 
< 0.1%
bethlehem 2
 
< 0.1%
georgetown 2
 
< 0.1%
beach 2
 
< 0.1%
fort 2
 
< 0.1%
houston 1
 
< 0.1%
port 1
 
< 0.1%
Other values (71) 71
 
1.4%
2024-10-14T20:54:56.009506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 4941
11.1%
a 4925
11.1%
o 4917
11.0%
n 4916
11.0%
r 4907
11.0%
h 4896
11.0%
c 4890
11.0%
g 4886
11.0%
A 4877
11.0%
l 52
 
0.1%
Other values (34) 300
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4941
11.1%
a 4925
11.1%
o 4917
11.0%
n 4916
11.0%
r 4907
11.0%
h 4896
11.0%
c 4890
11.0%
g 4886
11.0%
A 4877
11.0%
l 52
 
0.1%
Other values (34) 300
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4941
11.1%
a 4925
11.1%
o 4917
11.0%
n 4916
11.0%
r 4907
11.0%
h 4896
11.0%
c 4890
11.0%
g 4886
11.0%
A 4877
11.0%
l 52
 
0.1%
Other values (34) 300
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4941
11.1%
a 4925
11.1%
o 4917
11.0%
n 4916
11.0%
r 4907
11.0%
h 4896
11.0%
c 4890
11.0%
g 4886
11.0%
A 4877
11.0%
l 52
 
0.1%
Other values (34) 300
 
0.7%

zestimateLowPercent
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7141412
Minimum5
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-10-14T20:54:56.240128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7
Q18
median9
Q311
95-th percentile14
Maximum38
Range33
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4553925
Coefficient of variation (CV)0.25276475
Kurtosis9.197602
Mean9.7141412
Median Absolute Deviation (MAD)1
Skewness1.8226147
Sum48017
Variance6.0289522
MonotonicityNot monotonic
2024-10-14T20:54:56.417023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
9 1003
20.3%
10 904
18.3%
8 867
17.5%
11 619
12.5%
7 505
10.2%
12 345
 
7.0%
13 207
 
4.2%
6 127
 
2.6%
14 120
 
2.4%
5 72
 
1.5%
Other values (14) 174
 
3.5%
ValueCountFrequency (%)
5 72
 
1.5%
6 127
 
2.6%
7 505
10.2%
8 867
17.5%
9 1003
20.3%
10 904
18.3%
11 619
12.5%
12 345
 
7.0%
13 207
 
4.2%
14 120
 
2.4%
ValueCountFrequency (%)
38 1
 
< 0.1%
30 1
 
< 0.1%
26 1
 
< 0.1%
25 7
 
0.1%
24 2
 
< 0.1%
23 3
 
0.1%
22 6
 
0.1%
21 1
 
< 0.1%
20 6
 
0.1%
19 18
0.4%

timeOnZillow
Real number (ℝ)

Distinct2481
Distinct (%)50.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3777.7107
Minimum1
Maximum19949
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-10-14T20:54:56.614956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1335.1
Q13160
median3779
Q34394.5
95-th percentile6276.9
Maximum19949
Range19948
Interquartile range (IQR)1234.5

Descriptive statistics

Standard deviation1563.5603
Coefficient of variation (CV)0.41389095
Kurtosis8.655801
Mean3777.7107
Median Absolute Deviation (MAD)616
Skewness1.2216537
Sum18673224
Variance2444720.7
MonotonicityNot monotonic
2024-10-14T20:54:56.836715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3917 14
 
0.3%
3758 14
 
0.3%
3644 14
 
0.3%
3581 14
 
0.3%
3728 13
 
0.3%
4085 12
 
0.2%
3729 11
 
0.2%
4127 11
 
0.2%
3574 11
 
0.2%
3532 11
 
0.2%
Other values (2471) 4818
97.5%
ValueCountFrequency (%)
1 1
 
< 0.1%
4 1
 
< 0.1%
5 4
0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 3
0.1%
12 3
0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
19949 3
0.1%
11640 1
 
< 0.1%
11152 1
 
< 0.1%
11065 1
 
< 0.1%
10994 1
 
< 0.1%
10777 1
 
< 0.1%
10766 1
 
< 0.1%
10759 2
< 0.1%
10701 1
 
< 0.1%
10575 1
 
< 0.1%

zestimate
Real number (ℝ)

HIGH CORRELATION 

Distinct3198
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean400226.74
Minimum65000
Maximum2751800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-10-14T20:54:57.058440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum65000
5-th percentile157340
Q1296400
median388100
Q3469550
95-th percentile689240
Maximum2751800
Range2686800
Interquartile range (IQR)173150

Descriptive statistics

Standard deviation176022.86
Coefficient of variation (CV)0.43980785
Kurtosis13.705396
Mean400226.74
Median Absolute Deviation (MAD)86200
Skewness2.1119346
Sum1.9783208 × 109
Variance3.0984048 × 1010
MonotonicityNot monotonic
2024-10-14T20:54:57.291724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
388100 7
 
0.1%
462800 6
 
0.1%
411600 6
 
0.1%
420900 6
 
0.1%
375900 6
 
0.1%
403900 6
 
0.1%
394000 6
 
0.1%
419500 6
 
0.1%
350200 5
 
0.1%
417600 5
 
0.1%
Other values (3188) 4884
98.8%
ValueCountFrequency (%)
65000 1
< 0.1%
79700 1
< 0.1%
87100 1
< 0.1%
90100 2
< 0.1%
91000 1
< 0.1%
91500 1
< 0.1%
92400 1
< 0.1%
93800 1
< 0.1%
94600 1
< 0.1%
95600 1
< 0.1%
ValueCountFrequency (%)
2751800 1
< 0.1%
2058500 1
< 0.1%
1905800 1
< 0.1%
1835600 1
< 0.1%
1592300 1
< 0.1%
1556900 1
< 0.1%
1493700 1
< 0.1%
1493600 1
< 0.1%
1462700 1
< 0.1%
1445400 1
< 0.1%

livingArea
Real number (ℝ)

HIGH CORRELATION 

Distinct1860
Distinct (%)37.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1812.9887
Minimum1
Maximum14500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-10-14T20:54:57.499138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile744
Q11171.5
median1716
Q32147.5
95-th percentile3511.8
Maximum14500
Range14499
Interquartile range (IQR)976

Descriptive statistics

Standard deviation907.71194
Coefficient of variation (CV)0.5006716
Kurtosis16.452872
Mean1812.9887
Median Absolute Deviation (MAD)500
Skewness2.3742231
Sum8961603
Variance823940.97
MonotonicityNot monotonic
2024-10-14T20:54:57.736459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1040 67
 
1.4%
1920 51
 
1.0%
1824 47
 
1.0%
1872 43
 
0.9%
1728 39
 
0.8%
1976 32
 
0.6%
1200 31
 
0.6%
988 27
 
0.5%
1152 26
 
0.5%
1500 23
 
0.5%
Other values (1850) 4557
92.2%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
20 1
< 0.1%
320 1
< 0.1%
399 2
< 0.1%
400 1
< 0.1%
404 1
< 0.1%
415 1
< 0.1%
446 1
< 0.1%
450 1
< 0.1%
ValueCountFrequency (%)
14500 1
< 0.1%
13244 1
< 0.1%
8349 1
< 0.1%
7500 1
< 0.1%
7227 1
< 0.1%
7010 2
< 0.1%
7004 1
< 0.1%
6984 1
< 0.1%
6878 1
< 0.1%
6692 1
< 0.1%

zipcode
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98716.434
Minimum2649
Maximum99518
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-10-14T20:54:58.254058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2649
5-th percentile99501
Q199502
median99507
Q399515
95-th percentile99518
Maximum99518
Range96869
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7461.2715
Coefficient of variation (CV)0.075582871
Kurtosis107.03132
Mean98716.434
Median Absolute Deviation (MAD)5
Skewness-10.18448
Sum4.8795533 × 108
Variance55670572
MonotonicityNot monotonic
2024-10-14T20:54:58.520257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99507 900
18.2%
99502 838
17.0%
99508 737
14.9%
99517 607
12.3%
99504 405
8.2%
99518 396
8.0%
99501 348
 
7.0%
99515 248
 
5.0%
99503 240
 
4.9%
99516 156
 
3.2%
Other values (65) 68
 
1.4%
ValueCountFrequency (%)
2649 3
0.1%
2865 1
 
< 0.1%
6401 1
 
< 0.1%
6415 1
 
< 0.1%
6607 1
 
< 0.1%
8054 1
 
< 0.1%
8088 1
 
< 0.1%
8332 1
 
< 0.1%
10704 1
 
< 0.1%
11355 1
 
< 0.1%
ValueCountFrequency (%)
99518 396
8.0%
99517 607
12.3%
99516 156
 
3.2%
99515 248
 
5.0%
99508 737
14.9%
99507 900
18.2%
99504 405
8.2%
99503 240
 
4.9%
99502 838
17.0%
99501 348
 
7.0%

propertyTaxRate
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3070362
Minimum0
Maximum2.43
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-10-14T20:54:58.785425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.31
Q11.31
median1.31
Q31.31
95-th percentile1.31
Maximum2.43
Range2.43
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.070111319
Coefficient of variation (CV)0.053641451
Kurtosis157.39508
Mean1.3070362
Median Absolute Deviation (MAD)0
Skewness-5.6114206
Sum6460.68
Variance0.004915597
MonotonicityNot monotonic
2024-10-14T20:54:59.045079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.31 4874
98.6%
0.63 3
 
0.1%
0.59 2
 
< 0.1%
0.66 2
 
< 0.1%
1.14 2
 
< 0.1%
0.91 2
 
< 0.1%
1.36 2
 
< 0.1%
0 2
 
< 0.1%
0.31 2
 
< 0.1%
0.41 2
 
< 0.1%
Other values (46) 50
 
1.0%
ValueCountFrequency (%)
0 2
< 0.1%
0.31 2
< 0.1%
0.41 2
< 0.1%
0.42 2
< 0.1%
0.45 1
 
< 0.1%
0.57 1
 
< 0.1%
0.59 2
< 0.1%
0.61 1
 
< 0.1%
0.62 1
 
< 0.1%
0.63 3
0.1%
ValueCountFrequency (%)
2.43 1
< 0.1%
2.35 1
< 0.1%
2.22 2
< 0.1%
2.13 1
< 0.1%
1.89 1
< 0.1%
1.87 1
< 0.1%
1.85 1
< 0.1%
1.72 1
< 0.1%
1.69 1
< 0.1%
1.66 1
< 0.1%

bathrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0984422
Minimum0
Maximum30
Zeros24
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-10-14T20:54:59.258204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.5
median2
Q32.5
95-th percentile3.5
Maximum30
Range30
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.97919505
Coefficient of variation (CV)0.4666295
Kurtosis162.74267
Mean2.0984422
Median Absolute Deviation (MAD)0.5
Skewness6.8613747
Sum10372.6
Variance0.95882295
MonotonicityNot monotonic
2024-10-14T20:54:59.513685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2 1960
39.7%
1 932
18.9%
3 673
 
13.6%
2.5 580
 
11.7%
1.5 417
 
8.4%
4 133
 
2.7%
3.5 95
 
1.9%
5 32
 
0.6%
4.5 24
 
0.5%
0 24
 
0.5%
Other values (15) 73
 
1.5%
ValueCountFrequency (%)
0 24
 
0.5%
0.5 10
 
0.2%
1 932
18.9%
1.3 1
 
< 0.1%
1.5 417
 
8.4%
1.75 15
 
0.3%
1.8 1
 
< 0.1%
2 1960
39.7%
2.25 3
 
0.1%
2.5 580
 
11.7%
ValueCountFrequency (%)
30 1
 
< 0.1%
21 1
 
< 0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
7 3
 
0.1%
6.5 1
 
< 0.1%
6 11
 
0.2%
5.5 8
 
0.2%
5 32
0.6%
4.5 24
0.5%

bedrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2055432
Minimum0
Maximum30
Zeros8
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-10-14T20:54:59.768586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum30
Range30
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2507519
Coefficient of variation (CV)0.39018409
Kurtosis54.123993
Mean3.2055432
Median Absolute Deviation (MAD)1
Skewness3.5497933
Sum15845
Variance1.5643804
MonotonicityNot monotonic
2024-10-14T20:54:59.978923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 2091
42.3%
4 1203
24.3%
2 1000
20.2%
5 245
 
5.0%
1 200
 
4.0%
6 138
 
2.8%
7 25
 
0.5%
8 14
 
0.3%
10 11
 
0.2%
0 8
 
0.2%
Other values (4) 8
 
0.2%
ValueCountFrequency (%)
0 8
 
0.2%
1 200
 
4.0%
2 1000
20.2%
3 2091
42.3%
4 1203
24.3%
5 245
 
5.0%
6 138
 
2.8%
7 25
 
0.5%
8 14
 
0.3%
9 5
 
0.1%
ValueCountFrequency (%)
30 1
 
< 0.1%
21 1
 
< 0.1%
14 1
 
< 0.1%
10 11
 
0.2%
9 5
 
0.1%
8 14
 
0.3%
7 25
 
0.5%
6 138
 
2.8%
5 245
 
5.0%
4 1203
24.3%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct3196
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean400306.77
Minimum1250
Maximum2750000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-10-14T20:55:00.200010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1250
5-th percentile157120
Q1296400
median388200
Q3469550
95-th percentile689240
Maximum2750000
Range2748750
Interquartile range (IQR)173150

Descriptive statistics

Standard deviation176233.14
Coefficient of variation (CV)0.44024522
Kurtosis13.626887
Mean400306.77
Median Absolute Deviation (MAD)86200
Skewness2.105954
Sum1.9787164 × 109
Variance3.1058121 × 1010
MonotonicityNot monotonic
2024-10-14T20:55:00.466591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
388100 7
 
0.1%
420900 6
 
0.1%
375900 6
 
0.1%
419500 6
 
0.1%
403900 6
 
0.1%
462800 6
 
0.1%
394000 6
 
0.1%
411600 6
 
0.1%
417600 5
 
0.1%
412600 5
 
0.1%
Other values (3186) 4884
98.8%
ValueCountFrequency (%)
1250 1
< 0.1%
60000 1
< 0.1%
79700 1
< 0.1%
87100 1
< 0.1%
90100 2
< 0.1%
91000 1
< 0.1%
91500 1
< 0.1%
92400 1
< 0.1%
93800 1
< 0.1%
94600 1
< 0.1%
ValueCountFrequency (%)
2750000 1
< 0.1%
2058500 1
< 0.1%
1905800 1
< 0.1%
1835600 1
< 0.1%
1592300 1
< 0.1%
1556900 1
< 0.1%
1493700 1
< 0.1%
1493600 1
< 0.1%
1462700 1
< 0.1%
1445400 1
< 0.1%

zestimateHighPercent
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7857576
Minimum5
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-10-14T20:55:00.660461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7
Q18
median9
Q311
95-th percentile14
Maximum59
Range54
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8008973
Coefficient of variation (CV)0.28622182
Kurtosis29.525557
Mean9.7857576
Median Absolute Deviation (MAD)1
Skewness3.1715261
Sum48371
Variance7.8450258
MonotonicityNot monotonic
2024-10-14T20:55:00.889760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
9 1008
20.4%
8 917
18.6%
10 846
17.1%
11 548
11.1%
7 524
10.6%
12 339
 
6.9%
13 211
 
4.3%
6 126
 
2.5%
14 110
 
2.2%
15 92
 
1.9%
Other values (20) 222
 
4.5%
ValueCountFrequency (%)
5 70
 
1.4%
6 126
 
2.5%
7 524
10.6%
8 917
18.6%
9 1008
20.4%
10 846
17.1%
11 548
11.1%
12 339
 
6.9%
13 211
 
4.3%
14 110
 
2.2%
ValueCountFrequency (%)
59 1
 
< 0.1%
41 1
 
< 0.1%
33 1
 
< 0.1%
31 2
< 0.1%
30 1
 
< 0.1%
29 1
 
< 0.1%
28 1
 
< 0.1%
27 3
0.1%
26 4
0.1%
25 2
< 0.1%

homeType_APARTMENT
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.7 KiB
0
4899 
1
 
44

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4943
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4899
99.1%
1 44
 
0.9%

Length

2024-10-14T20:55:01.094625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-14T20:55:01.237778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4899
99.1%
1 44
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 4899
99.1%
1 44
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4899
99.1%
1 44
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4899
99.1%
1 44
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4899
99.1%
1 44
 
0.9%

homeType_CONDO
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.7 KiB
0
4192 
1
751 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4943
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 4192
84.8%
1 751
 
15.2%

Length

2024-10-14T20:55:01.398582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-14T20:55:01.561784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4192
84.8%
1 751
 
15.2%

Most occurring characters

ValueCountFrequency (%)
0 4192
84.8%
1 751
 
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4192
84.8%
1 751
 
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4192
84.8%
1 751
 
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4192
84.8%
1 751
 
15.2%

homeType_LOT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.7 KiB
0
4936 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4943
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4936
99.9%
1 7
 
0.1%

Length

2024-10-14T20:55:01.760750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-14T20:55:01.928582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4936
99.9%
1 7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 4936
99.9%
1 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4936
99.9%
1 7
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4936
99.9%
1 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4936
99.9%
1 7
 
0.1%

homeType_MANUFACTURED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.7 KiB
0
4932 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4943
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4932
99.8%
1 11
 
0.2%

Length

2024-10-14T20:55:02.112333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-14T20:55:02.252080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4932
99.8%
1 11
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 4932
99.8%
1 11
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4932
99.8%
1 11
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4932
99.8%
1 11
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4932
99.8%
1 11
 
0.2%

homeType_MULTI_FAMILY
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.7 KiB
0
4532 
1
 
411

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4943
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4532
91.7%
1 411
 
8.3%

Length

2024-10-14T20:55:02.448310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-14T20:55:02.598950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4532
91.7%
1 411
 
8.3%

Most occurring characters

ValueCountFrequency (%)
0 4532
91.7%
1 411
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4532
91.7%
1 411
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4532
91.7%
1 411
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4532
91.7%
1 411
 
8.3%

homeType_SINGLE_FAMILY
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.7 KiB
1
3613 
0
1330 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4943
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 3613
73.1%
0 1330
 
26.9%

Length

2024-10-14T20:55:02.766347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-14T20:55:02.938205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3613
73.1%
0 1330
 
26.9%

Most occurring characters

ValueCountFrequency (%)
1 3613
73.1%
0 1330
 
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3613
73.1%
0 1330
 
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3613
73.1%
0 1330
 
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3613
73.1%
0 1330
 
26.9%

Interactions

2024-10-14T20:54:47.844720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:38.735276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:43.710503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:46.925792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:49.390268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:54.671978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:00.726730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:06.110745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:10.993506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:15.953194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:20.916613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:24.896372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:29.342242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:32.794787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:36.629246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:40.402333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:43.885522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:48.010472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:39.135893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:43.984734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:47.092770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:49.556921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:54.901679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:01.072093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:06.575392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:11.213627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:16.455084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:21.173107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:25.108552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:29.574234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:33.111935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:36.848886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:40.644415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:44.083330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:48.185388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:39.474322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:44.217808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:47.223283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:49.705219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:55.148152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:01.360574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:06.854773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:11.422637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:16.902036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:21.392080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:25.318267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:29.804191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:33.376300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:37.037679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:40.846497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:44.301351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:48.324712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:39.779722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:44.414020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:47.361876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:49.837535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:55.344779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:01.606486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:07.157795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:11.641442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:17.180029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:21.584714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:25.471852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:29.985516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:33.583172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:37.233360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:41.048275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:44.460813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:48.475677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:40.017812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:44.570495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:47.503323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:50.007788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:55.664627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:01.857907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:07.430794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:11.926729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:17.696328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:21.809940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:25.657899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:30.177243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:33.843068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:37.465337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:41.276471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:44.658591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:48.669517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:40.318571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:44.713878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:47.657714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:50.156834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:56.024688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:02.123505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:07.694733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:12.361620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:18.036969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:22.030839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:25.863766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:30.389432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:34.078797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:37.766758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:41.494701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:44.839506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:48.818453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:40.788660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:44.916741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:47.794576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:50.369658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:56.450391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:02.354475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:07.997475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:12.989612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:18.399174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:22.277807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:26.178859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:30.581089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:34.304852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:38.029135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:41.710958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:45.022534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:48.956357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:41.125276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:45.090497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:47.949044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:50.626694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:56.873764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:02.584689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:08.281670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:13.413373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:18.683059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:22.517090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:26.439794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:30.744479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:34.505262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:38.245517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:41.893586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:45.191319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:49.097253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:41.417414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:45.289925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:48.106865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:51.518818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:57.171928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:02.808416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:08.547571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:13.790236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:18.990992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:22.912462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:26.729859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:30.935213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:34.734643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:38.502129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:42.086425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:45.380599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:49.276381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:41.716002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:45.470913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:48.251322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:51.889771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:58.329106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:03.051934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:08.937484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:14.107635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:19.242524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:23.147771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:27.037185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:31.151203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:34.944524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:38.742007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:42.304991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:45.628462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:49.401099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:41.942120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:45.633605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:48.381051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:52.324413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:58.684238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:03.242443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:09.251663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:14.368625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:19.461582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:23.343096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:27.411038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:31.343915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:35.165656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:38.946636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:42.484078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:45.858079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:49.566761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:42.144481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:45.792000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:48.540979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:52.613296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:59.005121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:03.898448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:09.509706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:14.671408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:19.687613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:23.543258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:28.108577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:31.524651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:35.400770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:39.132514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:42.683544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:46.140247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:49.728388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:42.721246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:45.992089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:48.670060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:52.926951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:59.251344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:04.108779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:09.763050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:14.891598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:19.940713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:23.776408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:28.289241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:31.693023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:35.600297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:39.318509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:42.888572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:46.381504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:49.876609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:42.952549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:46.192324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:48.793891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:53.249222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:59.577147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:04.411849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:10.027758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:15.067631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:20.103921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:23.980009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:28.468414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:31.908966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:35.800084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:39.501720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:43.096316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:46.588932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:50.039507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:43.099797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:46.440682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:48.968315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:53.632677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:59.888215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:04.673517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:10.286709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:15.240700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:20.293768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:24.201997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:28.674475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:32.137190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:36.016896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:39.715455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:43.277009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:47.202632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:50.205366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:43.258619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:46.604747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:49.114610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:53.973841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:00.194669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:04.931735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:10.492693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:15.449346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:20.485810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:24.409531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:28.919325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:32.357033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:36.225458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:40.033031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:43.480281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:47.423312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:50.379226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:43.557680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:46.757827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:49.254367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:53:54.395111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:00.451283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:05.513803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:10.759798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:15.668148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:20.670360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:24.599536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:29.146558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:32.557157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:36.422209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:40.214247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:43.689173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T20:54:47.632727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-14T20:55:03.105311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
annualHomeownersInsurancebathroomsbedroomscountyFIPShomeType_APARTMENThomeType_CONDOhomeType_LOThomeType_MANUFACTUREDhomeType_MULTI_FAMILYhomeType_SINGLE_FAMILYlatitudelivingArealongitudemonthlyHoaFeepricepropertyTaxRaterentZestimatestatetimeOnZillowyearBuiltzestimatezestimateHighPercentzestimateLowPercentzipcode
annualHomeownersInsurance1.0000.7110.662-0.0300.0820.6720.0000.0840.1570.522-0.2810.879-0.052-0.2571.000-0.0010.8370.206-0.0250.0711.000-0.047-0.175-0.014
bathrooms0.7111.0000.5920.0060.0000.0860.0000.0000.2370.056-0.2210.7550.007-0.0600.711-0.0130.7600.169-0.0600.2280.711-0.115-0.2160.024
bedrooms0.6620.5921.000-0.0570.0570.2900.0000.0000.4170.164-0.1100.7290.027-0.2780.6620.0360.6680.154-0.002-0.0370.662-0.076-0.1450.031
countyFIPS-0.0300.006-0.0571.0000.0000.0000.0000.1320.0000.018-0.190-0.0130.1900.032-0.030-0.289-0.0410.836-0.1070.073-0.0300.0430.015-0.192
homeType_APARTMENT0.0820.0000.0570.0001.0000.0340.0000.0000.0200.1530.0000.1040.0000.0000.0820.0000.1510.0000.0240.0340.0430.1420.1670.000
homeType_CONDO0.6720.0860.2900.0000.0341.0000.0000.0000.1260.6970.0210.4500.0000.0000.6710.0400.5500.0000.0310.2280.5390.0930.1730.000
homeType_LOT0.0000.0000.0000.0000.0000.0001.0000.0000.0000.0540.0000.2650.0000.0000.0000.0000.0500.0000.0000.0730.0000.5960.5740.000
homeType_MANUFACTURED0.0840.0000.0000.1320.0000.0000.0001.0000.0000.0720.2280.0260.2670.0000.0840.2370.0280.4190.0110.0000.0510.2620.2830.165
homeType_MULTI_FAMILY0.1570.2370.4170.0000.0200.1260.0000.0001.0000.4950.0000.2340.0110.0000.1570.0570.1450.0510.0400.1450.1700.0780.1210.000
homeType_SINGLE_FAMILY0.5220.0560.1640.0180.1530.6970.0540.0720.4951.0000.0300.3110.0470.0000.5210.0450.4820.0510.0640.1640.3830.1430.2030.011
latitude-0.281-0.221-0.110-0.1900.0000.0210.0000.2280.0000.0301.000-0.1930.165-0.042-0.2810.062-0.2930.8780.047-0.347-0.2820.2490.271-0.034
livingArea0.8790.7550.729-0.0130.1040.4500.2650.0260.2340.311-0.1931.0000.018-0.2140.8790.0160.8200.1040.0050.0150.8790.027-0.088-0.017
longitude-0.0520.0070.0270.1900.0000.0000.0000.2670.0110.0470.1650.0181.000-0.021-0.052-0.062-0.0610.9570.0390.051-0.0520.0160.0250.001
monthlyHoaFee-0.257-0.060-0.2780.0320.0000.0000.0000.0000.0000.000-0.042-0.214-0.0211.000-0.257-0.032-0.1840.000-0.0620.155-0.257-0.023-0.004-0.011
price1.0000.7110.662-0.0300.0820.6710.0000.0840.1570.521-0.2810.879-0.052-0.2571.000-0.0010.8370.206-0.0250.0711.000-0.047-0.175-0.014
propertyTaxRate-0.001-0.0130.036-0.2890.0000.0400.0000.2370.0570.0450.0620.016-0.062-0.032-0.0011.0000.0030.7760.056-0.033-0.000-0.032-0.0190.057
rentZestimate0.8370.7600.668-0.0410.1510.5500.0500.0280.1450.482-0.2930.820-0.061-0.1840.8370.0031.0000.138-0.0170.1260.837-0.115-0.208-0.005
state0.2060.1690.1540.8360.0000.0000.0000.4190.0510.0510.8780.1040.9570.0000.2060.7760.1381.0000.0560.4360.2190.1620.1960.967
timeOnZillow-0.025-0.060-0.002-0.1070.0240.0310.0000.0110.0400.0640.0470.0050.039-0.062-0.0250.056-0.0170.0561.000-0.033-0.0240.2530.2890.024
yearBuilt0.0710.228-0.0370.0730.0340.2280.0730.0000.1450.164-0.3470.0150.0510.1550.071-0.0330.1260.436-0.0331.0000.072-0.449-0.447-0.008
zestimate1.0000.7110.662-0.0300.0430.5390.0000.0510.1700.383-0.2820.879-0.052-0.2571.000-0.0000.8370.219-0.0240.0721.000-0.047-0.174-0.014
zestimateHighPercent-0.047-0.115-0.0760.0430.1420.0930.5960.2620.0780.1430.2490.0270.016-0.023-0.047-0.032-0.1150.1620.253-0.449-0.0471.0000.953-0.070
zestimateLowPercent-0.175-0.216-0.1450.0150.1670.1730.5740.2830.1210.2030.271-0.0880.025-0.004-0.175-0.019-0.2080.1960.289-0.447-0.1740.9531.000-0.054
zipcode-0.0140.0240.031-0.1920.0000.0000.0000.1650.0000.011-0.034-0.0170.001-0.011-0.0140.057-0.0050.9670.024-0.008-0.014-0.070-0.0541.000

Missing values

2024-10-14T20:54:50.621526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-14T20:54:51.142558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

longitudecountyFIPSmonthlyHoaFeeannualHomeownersInsurancestateyearBuiltlatituderentZestimatecityzestimateLowPercenttimeOnZillowzestimatelivingAreazipcodepropertyTaxRatebathroomsbedroomspricezestimateHighPercenthomeType_APARTMENThomeType_CONDOhomeType_LOThomeType_MANUFACTUREDhomeType_MULTI_FAMILYhomeType_SINGLE_FAMILY
0-149.908072020.00.02840AK1959.061.2173083142.0Anchorage12.03609.0676100.02668.0995011.312.03.067610013.0000001
1-149.908222020.00.02934AK1961.061.2171363113.0Anchorage12.02098.0698600.03179.0995011.312.03.069860013.0000001
2-149.908332020.00.04187AK1983.061.2170004282.0Anchorage12.03758.0996800.03059.0995011.313.04.099680014.0000001
3-149.908342020.00.02920AK1947.061.2167203458.0Anchorage15.03543.0695300.01642.0995011.312.05.069530016.0000001
4-149.907492020.00.04100AK2000.061.2171204161.0Anchorage18.03953.0976100.04483.0995011.314.04.097610024.0010000
5-149.907232020.00.02535AK2018.061.2170033943.0Anchorage11.03011.0603600.02560.0995011.313.53.060360013.0010000
6-149.907232020.00.03042AK1961.061.2171403318.0Anchorage11.01512.0724400.03224.0995011.313.06.072440012.0000010
7-149.905462020.00.01865AK1978.061.2183303591.0Anchorage11.01201.0444100.02087.0995011.313.02.044410012.0010000
8-149.910572020.00.0862AK1973.061.2145201945.0Anchorage13.05089.0205200.0899.0995011.311.02.020520013.0010000
9-149.910372020.00.01944AK1930.061.2153052128.0Anchorage12.03672.0462800.0678.0995011.311.01.046280013.0000001
longitudecountyFIPSmonthlyHoaFeeannualHomeownersInsurancestateyearBuiltlatituderentZestimatecityzestimateLowPercenttimeOnZillowzestimatelivingAreazipcodepropertyTaxRatebathroomsbedroomspricezestimateHighPercenthomeType_APARTMENThomeType_CONDOhomeType_LOThomeType_MANUFACTUREDhomeType_MULTI_FAMILYhomeType_SINGLE_FAMILY
4933-149.778442020.028.02314AK1974.061.1147543658.0Anchorage9.04243.0550900.02688.0995161.312.04.05509009.0000001
4934-149.780172020.028.01933AK1974.061.1147303427.0Anchorage9.03875.0460300.01872.0995161.312.03.04603009.0000001
4935-149.787952020.028.01835AK1974.061.1155783172.0Anchorage9.04057.0436800.01789.0995161.312.03.04368009.0000001
4936-149.788712020.028.01803AK1974.061.1142852845.0Anchorage9.03903.0429400.01496.0995161.312.53.042940010.0000001
4937-149.786062020.028.01916AK1974.061.1145633226.0Anchorage9.04164.0456300.01838.0995161.312.03.04563009.0000001
4938-149.784062020.028.02764AK1978.061.1150103903.0Anchorage10.01838.0658100.04263.0995161.312.53.065810010.0000001
4939-149.782962020.028.02160AK1974.061.1150783562.0Anchorage9.03925.0514200.02200.0995161.312.04.05142009.0000001
4940-149.752202020.045.02768AK1972.061.1243024917.0Anchorage11.03260.0659000.04180.0995071.314.55.065900012.0000001
4941-149.756582020.045.02979AK1972.061.1245464130.0Anchorage9.03728.0709400.03928.0995071.313.05.07094009.0000001
4942-149.757302020.045.02410AK1974.061.1246303566.0Anchorage9.03735.0573900.02576.0995071.312.54.057390010.0000001